In argumentative writing, writers must brainstorm hierarchical writing goals,
ensure the persuasiveness of their arguments, and revise and organize their
plans through drafting. Recent advances in large language models (LLMs) have
made interactive text generation through a chat interface (e.g., ChatGPT)
possible. However, this approach often neglects implicit writing context and
user intent, lacks support for user control and autonomy, and provides limited
assistance for sensemaking and revising writing plans. To address these
challenges, we introduce VISAR, an AI-enabled writing assistant system designed
to help writers brainstorm and revise hierarchical goals within their writing
context, organize argument structures through synchronized text editing and
visual programming, and enhance persuasiveness with argumentation spark
recommendations. VISAR allows users to explore, experiment with, and validate
their writing plans using automatic draft prototyping. A controlled lab study
confirmed the usability and effectiveness of VISAR in facilitating the
argumentative writing planning process.

本研究介绍了 VISAR，一个 AI 助手系统，用于协助写作者在他们的写作背景下构思和修订分层目标，并通过同步文本编辑和可视化程序组织论证结构，提高说服力。VISAR 还支持自动草稿制作，实验室实验证实了其在促进论证写作规划过程中的可用性和有效性。

VISAR: 具有可视化编程和快速草稿原型设计的人工智能辩论写作助手

VISAR: A Human-AI Argumentative Writing Assistant with Visual  Programming and Rapid Draft Prototyping

Users interact with text, image, code, or other editors on a daily basis.
However, machine learning models are rarely trained in the settings that
reflect the interactivity between users and their editor. This is
understandable as training AI models with real users is not only slow and
costly, but what these models learn may be specific to user interface design
choices. Unfortunately, this means most of the research on text, code, and
image generation has focused on non-interactive settings, whereby the model is
expected to get everything right without accounting for any input from a user
who may be willing to help.
We introduce a new Interactive Text Generation task that allows training
generation models interactively without the costs of involving real users, by
using user simulators that provide edits that guide the model towards a given
target text. We train our interactive models using Imitation Learning, and our
experiments against competitive non-interactive generation models show that
models trained interactively are superior to their non-interactive
counterparts, even when all models are given the same budget of user inputs or
edits.

介绍了在文本生成任务中，引入了一种使用用户模拟器进行交互式训练，使用模仿学习方法训练的交互式模型，证明这种方法比非交互式模型更好

交互式文本生成

Interactive Text Generation

A prevailing paradigm in neural text generation is one-shot generation, where
text is produced in a single step. The one-shot setting is inadequate, however,
when the constraints the user wishes to impose on the generated text are
dynamic, especially when authoring longer documents. We address this limitation
with an interactive text generation setting in which the user interacts with
the system by issuing commands to edit existing text. To this end, we propose a
novel text editing task, and introduce WikiDocEdits, a dataset of
single-sentence edits crawled from Wikipedia. We show that our Interactive
Editor, a transformer-based model trained on this dataset, outperforms
baselines and obtains positive results in both automatic and human evaluations.
We present empirical and qualitative analyses of this model's performance.

本研究通过提出一种交互式文本生成设置，在其中用户通过向系统发出编辑现有文本的命令与系统进行交互，来解决神经文本生成中一次生成的局限性，并介绍了一种新的文本编辑任务。通过使用 Wikipedia 中的单句编辑构成了一个名为 WikiDocEdits 的数据集，使用基于 transformer 的模型在其中进行训练以提高其自动产生的成果和用户评估结果。在此基础上，本研究分别从经验和定性分析方面展示了该模型的性能表现。